Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-d...
Shi-Jie Xiao, Jonathan Lin, Thomas Ehrmann et al.· 0 citations
A stretched-grid artificial intelligence weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.
Nazak Rouzegari, V. A. Gorooh, A. Sengupta et al.· 0 citations
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the current coarse state w...
Jurij Schönfeld, Tom Beucler, Julien Savre et al.· 0 citations
This work introduces data-driven integration kernels, a framework that adds structure to nonlocal operator learning by explicitly separating nonlocal information aggregation from local nonlinear prediction, and demonstrates the framework for South Asian monsoon precipitation using a hierarchy of neural network models w...
S. L. Ferretti, Jerry Lin, S. Shamekh et al.· Environmental Data Science· 0 citations
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A physics-inspired neural architecture that decomposes latent-state evolution into dedicated advection, diffusion, and reaction operators and achieves competitive deterministic forecast skill, with particularly strong performance at short to medium lead times, while preserving improved spectral fidelity and forecast ac...
Carlos A. Pereira, St'ephane Gaudreault, V. Dallerit et al.· arXiv.org· 5 citations
Despite their high accuracy on point-wise metrics, machine learning weather forecasting models can exhibit different failure modes such as blurring, periodic irregularities, and other unphysical spatial artifacts. This has motivated a variety of metrics to detect known failure cases. Existing metrics fix a representati...
Younes Elberkennou, Dmitri Demler, Thierry Meier et al.· 0 citations
Rapid and spatially continuous precipitation monitoring is critical for flood, landslide, and other hydrometeorological hazard warnings, particularly in regions where rain-gauge and weather-radar networks are sparse. The coordinated use of heterogeneous satellite observations, including geostationary infrared, passive...
Yun-Fan Yang, Hao-Fei Sun, Xiu-Yu Sun et al.· 0 citations
Near-surface weather varies over tens to hundreds of meters, yet remains unresolved in analyses and forecasts. We test whether this variation can be inferred without resolving atmospheric dynamics. Combining sparse weather stations, high-resolution Earth observation, and coarse atmospheric dynamics, we infer temperatur...
Jonathan Giezendanner, Qidong Yang, Ruizhe Huang et al.· 0 citations
We present IRENE (Italian Radar Ensemble Nowcasting Experiment), a deep learning model for probabilistic short-range precipitation nowcasting over the Italian domain at \SI{1}{km} spatial and 5 min temporal resolution. IRENE adopts an encoder--forecaster architecture built on multi-scale Convolutional Gated Recurrent U...
Alessandro Camilletti, Gabriele Franch, Elena Tomasi et al.· 0 citations
Medium-range weather forecasting underpins operational and planning decisions across the energy industry. Developing competitive weather models was once the domain of national meteorological centers, but recent advances in machine-learned weather prediction (MLWP) have opened the field to industry. We present Aries, a...
Lukas Hedegaard Morsing, A. Bakhtiarnia, J. Olesen et al.· 0 citations
Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated against high-fidelity references such as large-eddy simulations (LES). Existing approaches return point estimates and leave the associated uncertainty unquantified, a limitatio...
Luben M. C. Cabezas, Sacha Wendling, Aurèle Gallard et al.· 0 citations
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
